The Expanding Role of Artificial Intelligence in Health, Life, and Applied Sciences: A Multidisciplinary Review

Abstract

Artificial Intelligence (AI) has recently become a disruptive and integrated technology in health, life, and applied sciences, leading to transformative effects in research, diagnosis, decision-making, and automation. This cross-disciplinary review offers an overview of the evolution of AI systems from rule-based to recent machine learning, deep learning, and generative AI, and its role as an enabling technology. It discusses basic AI techniques such as supervised, unsupervised, and reinforcement learning, and the application of AI with different types of data, namely structured, unstructured, and multimodal data. The review provides a comprehensive, field-specific review of AI for health, biological, environmental, and life sciences. In the health sciences, AI helps diagnosis, drug discovery, medical image prediction, and treatment strategy; and in nutrition and biotechnology, predictive modelling and decision making. In the more general sense, AI has also aided in the areas of agriculture, environment, psychology, public health, and social policies through predictive analytics, pattern processing, and smart automation. Although the outlook is promising, incorporating AI in research and practice has ethical, legal, and social implications, such as privacy, bias, explainability, and regulatory challenges. Issues of data quality and generalisability, as well as a lack of infrastructure and skills, are other barriers. Future developments in explainable AI, federated learning, and multimodal AI systems are expected to lead to better transparency, co-operation, and multidisciplinary integration. The review calls for the need for ethical innovation and also highlights intercollaborative methods to harness AI for benefits. Overall, AI will not only serve as a technological enabler but also the backbone of science and society.